#Class 05: Data visualization
# use ggplot2 package
library(ggplot2) # load the package
head(cars)
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
# all ggplots have at least 3 layers
#data + aes + geoms
ggplot(data = cars) + aes(x = speed, y = dist) +
geom_point() +
# geom_line() +
geom_smooth(method = "lm") +
labs(title = "stopping dstance of old cars",
x = "speed (MPH)",
y = "stopping distance (ft)")
## `geom_smooth()` using formula 'y ~ x'
# ggplot is nothe only graphic system
plot(cars$speed, cars$speed)
plot(cars)
url <- "https://bioboot.github.io/bimm143_S20/class-material/up_down_expression.txt"
genes <- read.delim(url)
head(genes)
## Gene Condition1 Condition2 State
## 1 A4GNT -3.6808610 -3.4401355 unchanging
## 2 AAAS 4.5479580 4.3864126 unchanging
## 3 AASDH 3.7190695 3.4787276 unchanging
## 4 AATF 5.0784720 5.0151916 unchanging
## 5 AATK 0.4711421 0.5598642 unchanging
## 6 AB015752.4 -3.6808610 -3.5921390 unchanging
nrow(genes)
## [1] 5196
# how many genes are up?
table(genes$State)
##
## down unchanging up
## 72 4997 127
#what percentage of the genes are up?
# round() round up to whole number or certain digits
round(table(genes$State)/nrow(genes) *100, 3)
##
## down unchanging up
## 1.386 96.170 2.444
# make a figure
p <- ggplot(genes) + aes(x=Condition1, y=Condition2, col = State) +
geom_point()
p
#change the color
p + scale_color_manual(values = c("blue", "grey", "red"))
# bad color
p+ geom_point(col = "blue")
# nicer color
p + aes(color = State)
# explor the gapminder dataset
# install.packages("gapminder")
library(gapminder)
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
ggplot(gapminder,
aes(year, lifeExp, col = continent)) +
#geom_point(alpha = 0.4)+
geom_jitter(width = 0.3, alpha = 0.4) +
#geom_boxplot(alpha = 0.3, aes(group = year))
geom_violin(aes( group = year),alpha = 0.2, draw_quantiles = 0.5)
# install the plotly
# install.packages("plotly")
# interactive plot
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
ggplotly()